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    Description Logic EL++ Embeddings with Intersectional Closure

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    2202.14018.pdf
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    Description:
    Preprint
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    Type
    Preprint
    Authors
    Peng, Xi
    Tang, Zhenwei
    Kulmanov, Maxat cc
    Niu, Kexin
    Hoehndorf, Robert cc
    KAUST Department
    Bio-Ontology Research Group (BORG)
    Computational Bioscience Research Center (CBRC)
    Computer Science Program
    Computer, Electrical and Mathematical Science and Engineering (CEMSE) Division
    Date
    2022-02-28
    Permanent link to this record
    http://hdl.handle.net/10754/677971
    
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    Abstract
    Many ontologies, in particular in the biomedical domain, are based on the Description Logic EL++. Several efforts have been made to interpret and exploit EL++ ontologies by distributed representation learning. Specifically, concepts within EL++ theories have been represented as n-balls within an n-dimensional embedding space. However, the intersectional closure is not satisfied when using n-balls to represent concepts because the intersection of two n-balls is not an n-ball. This leads to challenges when measuring the distance between concepts and inferring equivalence between concepts. To this end, we developed EL Box Embedding (ELBE) to learn Description Logic EL++ embeddings using axis-parallel boxes. We generate specially designed box-based geometric constraints from EL++ axioms for model training. Since the intersection of boxes remains as a box, the intersectional closure is satisfied. We report extensive experimental results on three datasets and present a case study to demonstrate the effectiveness of the proposed method.
    Publisher
    arXiv
    arXiv
    2202.14018
    Additional Links
    https://arxiv.org/pdf/2202.14018.pdf
    Collections
    Bio-Ontology Research Group (BORG); Preprints; Computer Science Program; Computational Bioscience Research Center (CBRC); Computer, Electrical and Mathematical Science and Engineering (CEMSE) Division

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